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論文

Compact moving particle semi-implicit method for incompressible free-surface flow

Wang, Z.; 松本 俊慶; Duan, G.*; 松永 拓也*

Computer Methods in Applied Mechanics and Engineering, 414, p.116168_1 - 116168_49, 2023/09

 被引用回数:3 パーセンタイル:86.6(Engineering, Multidisciplinary)

Recently, consistent meshfree particle methods have been intensively studied. It has been pointed out that numerical inaccuracy or instability could easily occur with incomplete or biased neighbor support. This study proposes a new meshfree particle method called the compact moving particle semi-implicit (CMPS) method to decrease the condition number. In the proposed CMPS, the first-order and second-order derivatives are discretized separately, enhancing the numerical stability significantly. By adopting a small dilation parameter of the compact support, the CMPS can remarkably improve accuracy and reduce computational costs. Formulations for zeroth-order, first-order, and second-order derivatives are derived, and various boundary conditions, e.g., Dirichlet and Neumann, are discussed. In order to better deal with complex free-surface flows using the CMPS, some new numerical techniques, i.e., optimized regularization and reconstructed particle shifting schemes, are also developed. Furthermore, the surface fitting method is extended to address the surface tension. A convergence study is conducted in complex geometry to verify the stability, accuracy, and efficiency of the CMPS. Then, second-order accuracy is confirmed using the Taylor-Green vortex problem. After that, numerical examples concerning various free-surface flows, including square patch, hydrostatic pressure, dam break, droplet oscillation, and droplet coalescence, are calculated to demonstrate the potential of the CMPS.

論文

Automated Identification of Material Constants in Inelastic Constitutive Equations by Continuous Evolutionary Algorithm and Massively Parallel Processors

笠原 直人; 川崎 信史; 吉村 忍*; 矢川 元基*

Computer methods in applied mechanics and engineer, 0 Pages, 1999/00

生物の遺伝過程を模擬した遺伝的アルゴリズムは非線形性に強い最適化手法として注目されているが,対象物を離散化する必要がある。本研究は,遺伝的アルゴリズムを連続空間へ拡張することにより連続的進化的アルゴリズムを提案した。本アルゴリズムはランダム探索法の一種であるため,複雑な問題に適用した場合は膨大な計算時間を必要とする。このため,超並列計算機CRAY T-3とEWSのハラスタ結合システムで稼働する並立アルゴリズムを開発した。本手法の有効性を調べるため,人手では困難な非弾性構成方程式の材料定数同定問題に適用したところ,ほぼ100%の並列化効率により実用的な時間で解を得た。

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